



"""simple docstring"""





from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase							:       List[Any]          =       logging.get_logger(__name__)  # pylint: disable=invalid-name



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@register_to_config
		def __init__(       self				,     _lowercase				,     _lowercase = None				,     _lowercase = None )			->    str:







								'''simple docstring'''

								super().__init__()

								snake_case_ :			Optional[int]            =      learnable

								if self.learnable:
														assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
														assert length is not None, "learnable=True requires `length` to be set"

														snake_case_ :			Any            =      torch.zeros(_lowercase				,     _lowercase )
								else:
														snake_case_ :			List[str]            =      None

								snake_case_ :			Any            =      torch.nn.Parameter(_lowercase )




class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							42
		_lowerCamelCase      	=							42
		_lowerCamelCase      	=							42
		_lowerCamelCase      	=							42
		_lowerCamelCase      	=							42
		_lowerCamelCase      	=							42





		def __init__(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     )			->    Tuple:







								'''simple docstring'''

								super().__init__()

								self.register_modules(
								    vqvae=_lowercase				,     transformer=_lowercase				,     text_encoder=_lowercase				,     tokenizer=_lowercase				,     scheduler=_lowercase				,     learned_classifier_free_sampling_embeddings=_lowercase				,     )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase )			->    List[Any]:







								'''simple docstring'''

								snake_case_ :			List[str]            =      len(_lowercase ) if isinstance(_lowercase				,     _lowercase ) else 1

								# get prompt text embeddings
								snake_case_ :			List[str]            =      self.tokenizer(
								    _lowercase				,     padding="""max_length"""				,     max_length=self.tokenizer.model_max_length				,     return_tensors="""pt"""				,     )
								snake_case_ :			List[str]            =      text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														snake_case_ :			Tuple            =      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
														logger.warning(
														    """The following part of your input was truncated because CLIP can only handle sequences up to"""
														    f' {self.tokenizer.model_max_length} tokens: {removed_text}' )
														snake_case_ :			Optional[Any]            =      text_input_ids[:, : self.tokenizer.model_max_length]
								snake_case_ :			List[Any]            =      self.text_encoder(text_input_ids.to(self.device ) )[0]

								# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
								# While CLIP does normalize the pooled output of the text transformer when combining
								# the image and text embeddings, CLIP does not directly normalize the last hidden state.
								#
								# CLIP normalizing the pooled output.
								# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
								snake_case_ :			int            =      prompt_embeds / prompt_embeds.norm(dim=-1				,     keepdim=_lowercase )

								# duplicate text embeddings for each generation per prompt
								snake_case_ :			List[str]            =      prompt_embeds.repeat_interleave(_lowercase				,     dim=0 )

								if do_classifier_free_guidance:
														if self.learned_classifier_free_sampling_embeddings.learnable:
																				snake_case_ :			Any            =      self.learned_classifier_free_sampling_embeddings.embeddings
																				snake_case_ :			Tuple            =      negative_prompt_embeds.unsqueeze(0 ).repeat(_lowercase				,     1				,     1 )
														else:
																				snake_case_ :			Union[str, Any]            =      [""""""] * batch_size

																				snake_case_ :			Optional[Any]            =      text_input_ids.shape[-1]
																				snake_case_ :			str            =      self.tokenizer(
																				    _lowercase				,     padding="""max_length"""				,     max_length=_lowercase				,     truncation=_lowercase				,     return_tensors="""pt"""				,     )
																				snake_case_ :			Union[str, Any]            =      self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
																				# See comment for normalizing text embeddings
																				snake_case_ :			List[str]            =      negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1				,     keepdim=_lowercase )

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
														snake_case_ :			Optional[Any]            =      negative_prompt_embeds.shape[1]
														snake_case_ :			Optional[Any]            =      negative_prompt_embeds.repeat(1				,     _lowercase				,     1 )
														snake_case_ :			Any            =      negative_prompt_embeds.view(batch_size * num_images_per_prompt				,     _lowercase				,     -1 )

														# For classifier free guidance, we need to do two forward passes.
														# Here we concatenate the unconditional and text embeddings into a single batch
														# to avoid doing two forward passes
														snake_case_ :			List[Any]            =      torch.cat([negative_prompt_embeds, prompt_embeds] )

								return prompt_embeds





		@torch.no_grad()
		def __call__(       self				,     _lowercase				,     _lowercase = 1_0_0				,     _lowercase = 5.0				,     _lowercase = 1.0				,     _lowercase = 1				,     _lowercase = None				,     _lowercase = None				,     _lowercase = "pil"				,     _lowercase = True				,     _lowercase = None				,     _lowercase = 1				,     )			->    Union[ImagePipelineOutput, Tuple]:







								'''simple docstring'''

								if isinstance(_lowercase				,     _lowercase ):
														snake_case_ :			Dict            =      1
								elif isinstance(_lowercase				,     _lowercase ):
														snake_case_ :			Dict            =      len(_lowercase )
								else:
														raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(_lowercase )}' )

								snake_case_ :			str            =      batch_size * num_images_per_prompt

								snake_case_ :			str            =      guidance_scale > 1.0

								snake_case_ :			Optional[Any]            =      self._encode_prompt(_lowercase				,     _lowercase				,     _lowercase )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(_lowercase				,     _lowercase ) or callback_steps <= 0)
								):
														raise ValueError(
														    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
														    f' {type(_lowercase )}.' )

								# get the initial completely masked latents unless the user supplied it

								snake_case_ :			str            =      (batch_size, self.transformer.num_latent_pixels)
								if latents is None:
														snake_case_ :			str            =      self.transformer.num_vector_embeds - 1
														snake_case_ :			List[Any]            =      torch.full(_lowercase				,     _lowercase ).to(self.device )
								else:
														if latents.shape != latents_shape:
																				raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
														if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																				raise ValueError(
																				    """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
																				    f' {self.transformer.num_vector_embeds - 1} (inclusive).' )
														snake_case_ :			Any            =      latents.to(self.device )

								# set timesteps
								self.scheduler.set_timesteps(_lowercase				,     device=self.device )

								snake_case_ :			List[str]            =      self.scheduler.timesteps.to(self.device )

								snake_case_ :			List[str]            =      latents

								for i, t in enumerate(self.progress_bar(_lowercase ) ):
														# expand the sample if we are doing classifier free guidance
														snake_case_ :			Optional[Any]            =      torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample

														# predict the un-noised image
														# model_output == `log_p_x_0`
														snake_case_ :			Dict            =      self.transformer(_lowercase				,     encoder_hidden_states=_lowercase				,     timestep=_lowercase ).sample

														if do_classifier_free_guidance:
																				snake_case_      ,		snake_case_ :			List[str]            =      model_output.chunk(2 )
																				snake_case_ :			Optional[int]            =      model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																				model_output -= torch.logsumexp(_lowercase				,     dim=1				,     keepdim=_lowercase )

														snake_case_ :			Tuple            =      self.truncate(_lowercase				,     _lowercase )

														# remove `log(0)`'s (`-inf`s)
														snake_case_ :			List[str]            =      model_output.clamp(-7_0 )

														# compute the previous noisy sample x_t -> x_t-1
														snake_case_ :			Tuple            =      self.scheduler.step(_lowercase				,     timestep=_lowercase				,     sample=_lowercase				,     generator=_lowercase ).prev_sample

														# call the callback, if provided
														if callback is not None and i % callback_steps == 0:
																				callback(_lowercase				,     _lowercase				,     _lowercase )

								snake_case_ :			Any            =      self.vqvae.config.vq_embed_dim
								snake_case_ :			Tuple            =      (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
								snake_case_ :			Dict            =      self.vqvae.quantize.get_codebook_entry(_lowercase				,     shape=_lowercase )
								snake_case_ :			Any            =      self.vqvae.decode(_lowercase				,     force_not_quantize=_lowercase ).sample

								snake_case_ :			Tuple            =      (image / 2 + 0.5).clamp(0				,     1 )
								snake_case_ :			Any            =      image.cpu().permute(0				,     2				,     3				,     1 ).numpy()

								if output_type == "pil":
														snake_case_ :			Tuple            =      self.numpy_to_pil(_lowercase )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=_lowercase )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase )			->    torch.FloatTensor:







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			Union[str, Any]            =      torch.sort(_lowercase				,     1				,     descending=_lowercase )
								snake_case_ :			Optional[Any]            =      torch.exp(_lowercase )
								snake_case_ :			Dict            =      sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

								# Ensure that at least the largest probability is not zeroed out
								snake_case_ :			Optional[int]            =      torch.full_like(keep_mask[:, 0:1, :]				,     _lowercase )
								snake_case_ :			int            =      torch.cat((all_true, keep_mask)				,     dim=1 )
								snake_case_ :			Optional[Any]            =      keep_mask[:, :-1, :]

								snake_case_ :			List[Any]            =      keep_mask.gather(1				,     indices.argsort(1 ) )

								snake_case_ :			str            =      log_p_x_0.clone()

								snake_case_ :			str            =      -torch.inf  # -inf = log(0)

								return rv





"""simple docstring"""



def      UpperCAmelCase     (       _lowercase :   int = 1_0_0_0	)						-> int:


	"""simple docstring"""




	lowerCAmelCase_							,	lowerCAmelCase_      							=			1, 1
	lowerCAmelCase_      							=			[]
	for i in range(1			,   n + 1	):
		lowerCAmelCase_      							=			prev_numerator + 2 * prev_denominator
		lowerCAmelCase_      							=			prev_numerator + prev_denominator
		if len(str(_lowercase	)	) > len(str(_lowercase	)	):
			result.append(_lowercase	)
		lowerCAmelCase_      							=			numerator
		lowerCAmelCase_      							=			denominator

	return len(_lowercase	)


if __name__ == "__main__":
		print(f"""{solution() = }""")